class_docstr: Inference Dataset to perform prediction.
  
  Args:
      path (str | Path): Path to an image or image-folder.
      transform (A.Compose | None, optional): Albumentations Compose object describing the transforms that are
          applied to the inputs.
      image_size (int | tuple[int, int] | None, optional): Target image size
          to resize the original image. Defaults to None.
docstr: Get the image based on the `index`.
docstr_tok: ["Get","the","image","based","on","the","`","index","`","."]
code: def __getitem__(self, index: int) -> Any:
      """Get the image based on the `index`."""
      image_filename = self.image_filenames[index]
      image = read_image(path=image_filename)
      pre_processed = self.transform(image=image)
      pre_processed["image_path"] = str(image_filename)
  
      return pre_processed
  
file_path: anomalib/src/anomalib/data/inference.py
imports: from __future__ import annotations
  from pathlib import Path
  from typing import Any
  import albumentations
  from torch.utils.data.dataset import Dataset
  from anomalib.data.utils import get_image_filenames, get_transforms, read_image
label_desc: Use image node_id 3 for calling the InferenceDataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: pre_processed